How to Be the Boss of Your AI Assistant

Understanding LLMs & The Research Workflow

Author

Michael Borck

Welcome!

Today’s Journey

  • Understanding your AI assistant
  • Why structure beats magic
  • A proven 10-step research workflow
  • Live demonstration
  • Your roadmap to AI-powered research

Welcome students. Today we’re learning how to effectively manage AI tools for research. This isn’t about fancy prompts - it’s about understanding how these tools work.

Quick System Check! 📋

Do you have access to:

  • ✅ Microsoft Copilot (copilot.microsoft.com)
  • ✅ Internet connection
  • ✅ Ability to download files

Can’t Access Copilot?

  • Try ChatGPT (chat.openai.com) - free tier works
  • Claude (claude.ai) - also free
  • Key point: The principles work with ANY AI assistant

Timing: We’ll have troubleshooting breaks at 15 and 35 minutes!

The Problem: The “Everything” Prompt

Have you ever tried this?

  • You ask AI to do a huge task all at once…
  • “Analyse high-pressure processing for juice shelf life, tell me pros and cons, design an experiment for Vitamin C in orange juice, write the methods, and create expected results table”

What do you get back? 😕

  • ❌ Shallow, generic summary
  • ❌ Forgets half your instructions
  • ❌ Messy, unusable output

Real Example: “Write my entire literature review on plant proteins” → Gets 2 paragraphs of Wikipedia-level content

This happens because you’re giving the AI too much to think about at once. It’s like asking someone to juggle while solving math problems.

The Solution: Think Like a Manager 🎯

Not a Magician! ✨

Big Idea: Break complex research into small, logical steps

The Golden Rule - One Task, One Prompt

Give your AI assistant one clear job at a time

Let’s explore why this simple rule is so powerful…

Quick Start Essentials 📸

The Most Important Prompt Template:

You are an AI research scientist specializing in [YOUR FIELD].

Task: [ONE SPECIFIC TASK]

Requirements:
- [SPECIFIC REQUIREMENT 1]
- [SPECIFIC REQUIREMENT 2]
- [SPECIFIC REQUIREMENT 3]

Format: [HOW YOU WANT THE OUTPUT]

Context: [BACKGROUND INFO IF NEEDED]

Save this! 80% of your AI interactions will use this basic structure

Reason 1: Limited “Working Memory” 🧠

❌ The “Everything” Prompt

Scribbling 10 problems at once = No room for solutions

✅ The Step-by-Step Approach

One problem, full space = Detailed, accurate results

Takeaway: A single, focused task gets the AI’s full attention

LLMs have a context window - think of it as their working memory. When you fill it with multiple complex tasks, each gets less attention and processing power.

Technical Detail: Context Windows 📊

What’s really happening?

  • LLMs have finite context windows (8k-200k tokens)
  • Token ≈ 0.75 words
  • Each task competes for this limited space

Example:

  • LLM Context window: ~8,000 tokens
  • Complex research prompt: ~500 tokens
  • Response space needed: ~1,500 tokens per task
  • Result: Only 3-4 tasks fit properly!

Think of it like RAM: Too many programs = computer slows down. Too many tasks = AI quality drops.

This is why breaking tasks down isn’t just helpful - it’s technically necessary for quality outputs.

Reason 2: Guiding the AI’s “Thinking” 🧭

LLMs Create Answers Piece by Piece

  • Each word depends on previous words
  • Complex prompts = mental shortcuts
  • Structured prompts = logical reasoning

Cooking Analogy 👨‍🍳

❌ Bad: “Make beef wellington”

Chef might skip steps or use wrong ingredients

✅ Good: 1. “First, sear the beef” 2. “Next, prepare duxelles” 3. “Then, wrap in pastry”

By giving step-by-step instructions, you force the AI to build a logical argument, leading to much stronger outputs.

Reason 3: Easy Error Detection & Fixing 🔧

With a Giant Prompt

  • Problem: Weak experimental method
  • Solution: Re-run EVERYTHING
  • Time lost: 10-15 minutes
  • Quality: Hope for the best 🤞

With Our Workflow

  • Problem: Weak experimental method
  • Solution: Fix just that step
  • Time lost: 2 minutes
  • Quality: Keep what works ✓

Benefit: Iterative refinement = Higher quality + Less frustration

Reason 4: More and Better Ideas 💡

Diversity vs. Convergence

❌ Single Prompt Approach

“Give me the best hypothesis for oat milk fermentation” → One idea, possibly mediocre

✅ Our Multi-Step Approach

  1. Generate: “Give me 5 hypotheses” → Diversity
  2. Evaluate: “Score each for feasibility” → Analysis
  3. Select: “Recommend the top 3” → Quality

Result: Multiple perspectives + Critical evaluation = Stronger research

Good AI Outputs Look Like This:

✅ Structure & Detail - Organized sections - Specific numbers/examples - Academic language - Proper formatting

✅ Actionable Content - Clear next steps - Testable hypotheses - Realistic timelines - Measurable outcomes

Bad AI Outputs Look Like This:

❌ Vague & Generic - “Consider various factors…” - “This is an important topic…” - “Results may vary…”

❌ Incomplete - Missing key sections - No specific examples - Unclear methodology

The 10-Step Research Workflow 🔬

From Idea to Manuscript

Discovery Phase (Steps 1-5) ⏱️ ~60 minutes 1. Idea Generation - Brainstorm hypotheses (8 min) 2. Parallel Exploration - Diversify ideas (12 min) 3. Preliminary Testing - Feasibility checks (10 min) 4. Optimization - Find best parameters (15 min) 5. Full Execution - Main study design (15 min)

The 10-Step Research Workflow (cont’d) 📝

From Idea to Manuscript

Communication Phase (Steps 6-10) ⏱️ ~40 minutes

  1. Component Analysis - What matters most? (8 min)
  2. Visualization - Create figures & charts (10 min)
  3. Writing - Draft manuscript (12 min)
  4. Review - Peer review simulation (5 min)
  5. Iteration - Continuous improvement (5 min)

Total time: ~100 minutes for a complete research project from idea to first draft!

This mirrors the actual scientific method - we’re just using AI as our assistant!

Step 1: Idea Generation 🌱

The Power of Structured Brainstorming

Prompt Template:

You are an AI research scientist specializing in Food Science.
Given the following research area, generate 5 distinct and 
innovative scientific hypotheses suitable for a Masters-level 
research paper.

For each hypothesis, include:
- A clear Title
- 3-5 Keywords
- A short Abstract (under 200 words)
- An explanation of its Novelty and Significance

Research Area: [YOUR TOPIC HERE]

Pro tip: Replace “Food Science” with your specific field for better results!

Notice how specific this prompt is. We’re not just asking for “ideas” - we’re asking for structured, academic hypotheses.

Step 2-3: Exploration & Feasibility 🔍

Step 2: Parallel Exploration (12 min)

  • Open multiple chat sessions
  • Generate non-overlapping ideas
  • Score and rank all options
  • Output: 10-15 diverse hypotheses

Step 3: Preliminary Testing (10 min)

  • Select top hypothesis
  • Design minimal experiment
  • Generate expected data
  • Output: Feasibility confirmed

Key insight: Step 2 prevents tunnel vision - you see ALL possibilities before committing!

Steps 4-6: The Research Core 🔬

Building Your Study

Step 4: Optimization (15 min) - Test variable combinations - Define success criteria - Find the “sweet spot”

Step 5: Full Execution (15 min) - Detailed methodology - Comprehensive data tables - Statistical measures

Step 6: Component Analysis (8 min) - What ingredients matter? - Ablation studies - Understanding mechanisms

Steps 7-10: Communication & Refinement 📊

Step 7: Visualization (10 min) - Generate scientific figures - Write clear captions - Visual storytelling

Step 8: Manuscript Writing (12 min) - Complete paper draft - All sections included - Proper citations

Step 9: Peer Review (5 min) - Critical evaluation - Scoring rubric - Actionable feedback

Step 10: Iteration (5 min) - Address weaknesses - Refine sections - Achieve excellence

When AI Goes Wrong: Real Examples 🚨

Hallucination Alert!

Made-up Citations: > “According to Smith et al. (2023), fermentation at 45°C increases yield by 23%”

Reality: Paper doesn’t exist!

Plausible but Wrong Data: > “Oat milk contains 15g protein per 100ml”

Reality: Usually 1-3g per 100ml

What You Should Do:

  1. Always verify numerical claims
  2. Check citations before using them
  3. Cross-reference with reliable sources
  4. Use AI for structure, not facts

Remember: AI is confident even when wrong!

Ethical Guidelines: Using AI in Academic Work 📜

The Right Way to Cite AI Assistance

In your methods section: > “Hypothesis generation and experimental design were developed with assistance from Microsoft Copilot (Microsoft Corporation, 2024). All outputs were verified against peer-reviewed literature.”

What Requires Citation:

  • ✅ Idea generation
  • ✅ Statistical analysis suggestions
  • ✅ Writing structure
  • ✅ Data interpretation ideas

What Doesn’t:

  • Grammar checking
  • Simple calculations
  • Basic formatting

Bottom line: When in doubt, cite it. Transparency builds trust.

Live Demo Time! 🚀

Your Choice: Which Research Should We Explore?

Option A: 🥛 Fermentation Study - Oat milk fermentation - pH, cell counts, viscosity - 3 treatments over 48 hours

Option B: 📅 Shelf Life Analysis - Yogurt alternatives - Microbial & sensory data - 4 products over 30 days

Option C: 👅 Sensory Panel - Plant-based cheese - 50 panelists - Texture, flavor, preference

Option D: 🧪 Process Optimization - Protein extraction yields - Temperature vs pH effects - Response surface data

🗳️ Vote Now!

Scan QR code or shout out your choice!

Quick Demo: Bad vs. Good Prompting 🎯

Let’s try both approaches… (10 minutes total)

First: The “Everything” Prompt (3 min)

Watch what happens when we ask for too much at once

“Analyze [winning dataset], create graphs, write conclusions, and suggest future research”

Then: Our Structured Approach (7 min)

See the difference when we break it down

Steps 1 → 3 → 7 in sequence

Pay attention to: Response quality, detail level, and actionability

Troubleshooting Common Issues 🔧

When Things Don’t Work

Problem: AI gives generic responses - Solution: Add more specific requirements to your prompt

Problem: Getting confused by long conversations - Solution: Start a new chat session

Problem: AI refuses to help - Solution: Rephrase more academically, avoid trigger words

Universal fix: When stuck, start fresh with a clearer, more specific prompt

Understanding AI Limitations ⚠️

Critical Things to Know

Hallucinations - AI can “make up” information - Fake citations are common - Always verify sources - May invent plausible-sounding data

Other Limitations - Knowledge cutoff dates - Can’t access real databases - No actual lab work - Context window limits

Golden Rule for Research

Never trust, always verify! Use AI for ideation and structure, but validate all facts, citations, and data.

Key Takeaways 🎓

  1. One Task, One Prompt - Your golden rule
  2. Structure = Success - Guide the AI step-by-step
  3. Iterate & Refine - Fix what needs fixing
  4. Think Like a Manager - You’re the boss!
  5. Always Verify - AI assists, you validate

Next Session Preview 👀

  • Hands-on practice with all 10 steps
  • Building your own AI research agent
  • Advanced techniques and shortcuts
  • Bring a research topic you’re curious about!

Your Mission (Should You Choose to Accept) 🎯

Before Next Session:

  1. Think of a research topic you’re interested in
  2. Try the idea generation prompt on your own
  3. Note what works and what doesn’t
  4. Bookmark copilot.microsoft.com or your preferred AI tool

Remember

You’re not learning to use AI - you’re learning to manage AI

Questions? 🤔

Let’s Discuss!

  • Concerns about the workflow?
  • Technical questions?
  • Want to see another demo?
  • Ethical considerations?

📧 Contact: michael.borck@curtin.edu.au

Bonus Slide: The Complete Workflow 📋

Your Research Assistant Checklist

Step Task Time Output
1 Idea Generation 8 min 5 hypotheses table
2 Parallel Exploration 12 min 10-15 total ideas
3 Feasibility Testing 10 min Experimental plan + data
4 Optimization 15 min Best parameters
5 Full Study 15 min Complete methodology
6 Component Analysis 8 min Key factors identified
7 Visualization 10 min Figures + captions
8 Writing 12 min Full paper draft
9 Review 5 min Feedback report
10 Iteration 5 min Refined manuscript

This is a reference slide students can photograph or refer back to.